1 files changed, 37 insertions, 21 deletions

diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
index 78f408fc..329a46f6 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,8 @@
     "import ipywidgets as widgets\n",
     "from pyclustering.cluster.kmedoids import kmedoids\n",
     "from pyclustering.utils.metric import distance_metric, type_metric\n",
-    "import random"
+    "import random\n",
+    "import numpy as np"
    ]
   },
   {
@@ -176,23 +177,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.046150929558524685\n"
+      "average distance:  0.04615092955852465\n",
+      "std:  0.017305709419913242\n",
+      "max: 0.1411706837186424\n",
+      "min: 0.0\n"
      ]
     }
    ],
    "source": [
-    "total_distance = 0\n",
-    "count = 0\n",
+    "distances = []\n",
     "for model in models:\n",
-    "    total_distance += ks_value(od_rep_model.out_d, model.out_d)\n",
-    "print(total_distance / len(models))"
+    "    distances.append(ks_value(od_rep_model.out_d, model.out_d))\n",
+    "print('average distance: ', np.mean(distances))\n",
+    "print('std: ', np.std(distances))\n",
+    "print('max:', max(distances))\n",
+    "print('min:', min(distances))"
    ]
   },
   {
@@ -217,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -245,16 +251,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.04679429311806747\n"
+      "average distance:  0.046794293118067494\n",
+      "std:  0.02880119213919405\n",
+      "max: 0.18702970297029703\n",
+      "min: 0.0\n"
      ]
     }
    ],
    "source": [
-    "total_distance = 0\n",
-    "count = 0\n",
+    "distances = []\n",
     "for model in models:\n",
-    "    total_distance += ks_value(na_rep_model.na, model.na)\n",
-    "print(total_distance / len(models))"
+    "    distances.append(ks_value(na_rep_model.na, model.na))\n",
+    "print('average distance: ', np.mean(distances))\n",
+    "print('std: ', np.std(distances))\n",
+    "print('max:', max(distances))\n",
+    "print('min:', min(distances))"
    ]
   },
   {
@@ -279,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -300,23 +311,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.07028909225833631\n"
+      "average distance:  0.07028909225833632\n",
+      "std:  0.03728189051222417\n",
+      "max: 0.21961550993809065\n",
+      "min: 0.0\n"
      ]
     }
    ],
    "source": [
-    "total_distance = 0\n",
-    "count = 0\n",
+    "distances = []\n",
     "for model in models:\n",
-    "    total_distance += ks_value(mpc_rep_model.mpc, model.mpc)\n",
-    "print(total_distance / len(models))"
+    "    distances.append(ks_value(mpc_rep_model.mpc, model.mpc))\n",
+    "print('average distance: ', np.mean(distances))\n",
+    "print('std: ', np.std(distances))\n",
+    "print('max:', max(distances))\n",
+    "print('min:', min(distances))"
    ]
   },
   {